Adversarial training seems to be a simplistic strategy to provide robustness against adversarial evasion attacks; however, it has some cons. First, increasing the number of samples in the auxiliary data may not have a linear relationship with detection accuracy [1]}. Second, the adversarial training can not guarantee a robust defence as it can be bypassed [2]}. Although adopted as an immediate remedy against some adversarial attacks, it can not be considered an ultimate cure for the grave problem in AI. Third, it consumes additional time for retraining. Several GAN-based research works preserve the functionality of the generated samples by manipulating only non-functional features [3]}, [4]}, [5]}. So, GANs do not play a role in generating a complete feature vector in those works. It is also quite challenging to generate categorical features using a GAN without manual engineering except using a sequence GAN [6]}. Researchers have also used deep reinforcement learning to generate the functionality preserving adversarial evasion attacks [7]}, [8]}, [9]}. The main goal of employing deep reinforcement learning is to explore functionality preserving adversarial samples since the DRL can guarantee semantic awareness in contrast to GANs; however, these works consider the adversarial training to make the detection models adept at evasion awareness.
